As it is known, in an ever increasing number of applications is the widespread use of sensors networks for acquiring various kinds of data and for transmitting such data to a remote processing unit. Just to mention a few examples, the use of sensor networks has proven itself particularly advantageous in the security-systems field, in bio-medical applications, in the environment-control field, and in the transport field. In many applications, the sensor networks used are wireless networks, often referred to by the WSN acronym (Wireless Sensors Network).
It is known that in sensor networks, primarily in wireless networks, reducing the area or volume occupied by circuits or components included in the sensors is often a system requirement. For instance, presently in sensor wireless networks are generally used micro-sensors, or MEMS (an acronym of the expression Micro Electro-Mechanical Sensors), realized by CMOS technology, and in the future one envisages a use of sensors realized by sub-micrometric sophisticated technology CMOS (DSM CMOS).
In sensor networks, primarily in wireless networks, another system restraint is a low-energy consumption of the various system components supplied by a battery, with the purpose to maximize the battery life.
In sensor networks, and particularly in wireless networks, to limit the energy required to transmit the sensor output via signals to the remote processing unit, a transmission technique is known, that envisages grouping the sensors provided for acquiring data concerning homogeneous quantities, in such a way as to form groups of sensors. Each of such groups is associated with a local processing unit (that, generally, may be integrated in a sensor of the group), provided for receiving signals output by the sensors, processing such signals to form an aggregate signal, and transmitting such aggregate signal to the remote processing unit. The local processing unit generally comprises a micro-controller with very low power dissipation (MCU, Ultra-low-power Micro Controller) adapted to execute the above described operations.
A specific example of the above indicated transmission technique is disclosed in a paper by A. Wang, W. Einzelam and A. P. Chandrakasan entitled “Energy Scalable Protocols for Battery-Operated MicroSensor network” Kluwer Journal of VLSI Signal Processing, pp. 223-239, November 2001, which is incorporated by reference.
In the above-described transmission technique, the aggregate signal is obtained from the local processing unit by a merge (also referred to in the field by the word “beamforming”) of signals output by various sensors of the group, exploiting the redundancy present in such signals. In the above cited paper, such merge is obtained by adaptive equalization. In other techniques, the merge is obtained by a Kalman filtering.
The above-described beamforming techniques require the local processing unit to perform computations of significant complexity, such as for instance arithmetic operations in fixed or floating point. This implies a sizeable power consumption in the local processing unit, which, in practice, is forced to operate as a digital signal processor (DSP).